A Study of the Computational Space of Facial Expressions of Emotion

面部表情情感的计算空间研究

• 批准号: 8266468
• 负责人: Aleix M Martinez
• 金额: $ 36.6万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8266468
• 关键词: Acute; Address; Affect; Aging-Related Process; Anger; Arts; Autistic Disorder; Behavior; Child Abuse; Classification; Code; Cognition; Cognitive; Complex; Computer Simulation; Computer Vision Systems; Conscious; Cues; Depressed mood; Dimensions; Duchenne muscular dystrophy; Emotions; Evolution; Eye diseases; Face; Face Processing; Facial Expression; Facial Muscles; Fright; Goals; Happiness; Human; Huntington Disease; Image; Individual; Lead; Movement; Muscle; Neurons; Oral cavity; Perception; Play; Positioning Attribute; Primates; Process; Protocols documentation; Research; Resolution; Role; Schizophrenia; Shapes; Social Interaction; System; Time; To specify; Visual; Visual impairment; Visual system structure; base; cognitive system; computer human interaction; computer studies; court; design; human subject; millisecond; psychologic; research study; showing emotion; visual process; visual processing

Project Summary Past research has been very successful in defining how facial expressions of emotion are produced, including which muscle movements create the most commonly seen expressions. These facial expressions of emotion are then interpreted by our visual system. Yet, little is known about how these facial expressions are recognized. The overarching goal of this proposal is to define the form and dimensions of the cognitive (computational) space used in this visual recognition. In particular, this proposal will study the following three hypotheses: Although facial expressions are produced by a complex set of muscle movements, expressions are generally easily identified at different spatial and time resolutions. However, it is not know what these limits are. Our first hypothesis (H1) is that recognition of facial expressions of emotion can be achieved at low resolutions and after short exposure times. In Aim 1, we define experiments to determine how many pixels and milliseconds (ms) are needed to successfully identify different emotions. The fact that expressions of emotion can be recognized quickly at low resolution indicates that simple features robust to image manipulation are employed. Our second hypothesis (H2) is that the recognition of facial expressions of emotion is partially accomplished by an analysis of configural features. Configural cues are known to play an important role in other face recognition tasks, but their role in the processing of expressions of emotion is not yet well understood. Aim 2 will identify a number of these configural cues. We will use real images of faces, manipulated versions of these face images, and schematic drawings. It is also known that shape features play a role in facial expressions (e.g., the curvature of the mouth in happiness). In Aim 3, we define a shape-based computational model. Our hypothesis (H3) is that the configural and shape features are defined as deviations from a mean (or norm) face as opposed to being described as a set of independent exemplars (Gnostic neurons). The importance of this computational space is not only to further justify the results of the previous aims, but to make new predictions that can be verified with additional experiments with human subjects.

项目摘要 过去的研究已经非常成功地定义了情绪的面部表情是如何产生的，包括 哪种肌肉运动创造了最常见的表情。这些面部表情 然后被我们的视觉系统解读。然而，人们对这些面部表情是如何产生的知之甚少， 认可.这一建议的首要目标是确定认知的形式和维度， （计算）空间在这个视觉识别中使用。特别是，本建议将研究以下三个方面 假设：虽然面部表情是由一组复杂的肌肉运动产生的，但表情 通常容易在不同的空间和时间分辨率下识别。但是，不知道这些是什么 限制是。我们的第一个假设（H1）是，情绪的面部表情的识别可以实现在低 分辨率和短曝光时间后。在目标1中，我们定义实验来确定 并且需要毫秒（ms）来成功地识别不同的情绪。事实上， 情感可以在低分辨率下快速识别，表明简单特征对图像具有鲁棒性 操纵被使用。我们的第二个假设（H2）是， 情感部分是通过对图形特征的分析来实现的。众所周知，配置提示会在 在其他面部识别任务中起重要作用，但在处理情感表达中的作用并不重要。 但很好理解。目标2将识别出许多这样的神经线索。我们将使用真实的人脸图像， 这些面部图像的操纵版本和示意图。众所周知，形状特征 面部表情中的角色（例如，幸福时嘴角的弧度）。在目标3中，我们定义了基于形状的 计算模型我们的假设（H3）是，将轮廓和形状特征定义为偏差 从一个平均（或规范）面对，而不是被描述为一组独立的范例（诺斯替 神经元）。这个计算空间的重要性不仅在于进一步证明了前面的结果 目标，但作出新的预测，可以验证与人类受试者的额外实验。